dba africa management review

DBA AFRICAMANAGEMENTREVIEW

A Quarterly publication of the Department of Business Administration, School of Business,

University of Nairobi

I S S N N O : 2 2 2 4 - 2 0 2 3

M O D E R A T I O N E F F E C T O F M A C R OE N V I R O N M E N T O N T H E R E L A T I O N S H I P

B E T W E E N C O M P E T I T I V E S T R A T E G YD R I V E R S A N D P E R F O R M A N C E O F

M A N U F A C T U R I N G S M A L L A N DM E D I U M E N T E R P R I S E S I N N A I R O B I

C O U N T Y , K E N Y A

V I C T O R L A I B U N I B A A R I UD R . J A M E S G A T H U N G U , P H D , C P S ( K )

P R O F . B I T A N G E N D E M O . P H D

V O L U M E 1 0 N O . 5

2 0 2 0

http://journals.uonbi.ac.ke/damr ISSN - 2224-2023

November 2020 Vol 10 No 5 Pgs 107-133

107 | All rights reserved Department of Business Administration School of Business University of Nairobi DBA Africa Management Review

Received Date

12/11/2020

Accepted Date

23/11/2020

MODERATION EFFECT OF MACRO ENVIRONMENT ON THE

RELATIONSHIP BETWEEN COMPETITIVE STRATEGY DRIVERS

AND PERFORMANCE OF MANUFACTURING SMALL AND

MEDIUM ENTERPRISES IN NAIROBI COUNTY, KENYA

Victor Laibuni Baariu1, Dr. James Gathungu, PhD, CPS (K)2, Prof. Bitange

Ndemo. PhD3 Abstract

This study establishes the moderating effect of macro environment on the relationship between

competitive strategy drivers and the performance of small and medium enterprises (SMEs) in the

manufacturing sector in Nairobi City County in Kenya. SMEs are the backbone of many

economies through creation of employment opportunities as well as wealth creation for

entrepreneurs. Strong competitive strategy drivers have been found to enable SMEs achieve

favorable performance and remain competitive in their own markets. The macro environment

could have a significant moderating effect in the relationship between competitive strategy drivers

and firm performance. This study was based on Resource-Based theory which was augmented by

the open systems theory, combining to provide a framework for examining the moderating effect

of macro environment on the relationship between strategy drivers and performance of

manufacturing SMEs in Nairobi County, Nairobi. For the methodology, a cross-sectional survey

was done covering 334 manufacturing small and medium enterprises in Nairobi County, Kenya.

Structured questionnaires were used for data collection with a response rate of 89.6%. Various

descriptive statistics were used to project the demographic characteristics of the association and

the respondents. Inferential statistics was used to build up the connection between the factors and

test the model. The results of the study on the moderating effect of macro environment on the

relationship between competitive drivers and firm performance showed a very strong

relationship, implying that the competitive strategy drivers depend on macro environment in

determining the performance of manufacturing SMEs and consequently supporting the

formulated hypothesis. The results suggest that manufacturing SMEs need to edge their

operations by considering macro environment dynamics as they undertake their service and

product development. The findings in this research will have implications on business

management practices as well as be beneficial to business owners by way of improving their

management practice, for instance on the importance by manufacturing SMEs to scan their

political, economic, social, technological, ecological environment as they adopt various competitive

strategy drivers so as to enhance their performance. The researcher recommends similar research

to be undertaken in other SMEs and also have more managers respond to the questionnaires in

order to enrich the collected data.

Key Words: Competitive strategy drivers, Environment based drivers, Hybrid Strategy drivers, Value

chain, Performance, Macro Environment

1 School of Business, University of Nairobi, Kenya 2 School of Business, University of Nairobi, Kenya 3 School of Business, University of Nairobi, Kenya

DBA Africa Management Review




Introduction

SMEs are central to achieving successful

economic growth through promotion of

innovation, creation of employment

opportunities, sharpening of

entrepreneurship skills and supporting

social integration (Dahmen & Rodriguez,

2014). This recognition is derived from the

knowledge that they fuel economic growth

in most economies and if their performance

is undermined, economic development

suffers significantly (Sidik, 2012). Nairobi

County has the largest concentration of

microenterprises in Kenya, accounting for

approximately 25% of total employment in

the sector (KNBS, 2013).

According to Salavou (2015), a strategy

driver is a deliberate set of clearly defined

activities that are planned and implemented

with the aim of achieving a competitive

advantage. A manufacturing SME can

improve or erode its market position within

its industry through the strategy it adopts. In

furtherance of this view, Peteraf (1993)

emphasizes that the competitive strategy

drivers ought to be aligned to a firm’s long-

term strategy in an endeavor to achieve a

competitive position and achieve long term

profitability. The manufacturing SMEs

competitive strategy drivers in this study are

categorized as environmental-based

strategy drivers, resource-based strategy

drivers and hybrid strategy drivers.

Environmental drivers include cost

leadership and (product) differentiation. On

the resource-based theory, manufacturing

SMEs must optimize their resources if they

are to sustain a competitive advantage.

Gathungu and Baariu (2018) identify these

resources as human resources, intellectual

property, materials and organization brand

as well as capabilities, such as

innovativeness, efficiency and quality. For

this study, the resource-based strategy

drivers comprise of manufacturing SMEs

capital-raising capacity, technology

development, human capital and value

chain. The hybrid strategy drivers are

classified as a blend of low cost and

differentiation. The hybrid strategy is a

more complex strategic driver compared to

generic strategic approaches because it

involves a number of strategic focuses.

The resource-based theory argues that a

firm’s competitive advantage is mainly

derived from its ability to mobilize

resources to its advantage (Barney, 1991),

and hence the need to carefully identify,

evaluate and select competitive strategy

drivers to drive performance. According to

open systems theory of entrepreneurship, as

enterprises perform their trades, they will be

subjected to events and changes in their

macro environments. Recognizing

organizations as open entities, there is need

for their careful management to gratify and

stabilize internal needs and enable

adaptation to macro circumstances (Burnes,

2000). The open systems theory identifies

the surrounding environment as having an

impact on a firm’s change and survival.

This is so since enterprises are environment

serving and reliant (Ansoff & McDonell,

1990). The theory also explains how

strategy helps an organization to achieve

sustainable competitive advantage.

A firm’s macro environment as defined by

Hitt, Ireland and Hoskisson (2011) is a

blend of business environmental factors that

influence the firm’s operations or

functioning. Since the dynamics in the

macro environment are continuously

evolving, there should be regular re-

assessments of business strategies (Adeoye

& Elegunde, 2012). Macro environment is

central to SMEs because it offers business

opportunities. Risks and prospects in the

macro environment exist in the form of




different types of threats and challenges

facing SMEs. Macro environment is also an

important source of resources that firms

need for its day to day operations. For this

study, macro environment has been

represented by Political, Economic, Social,

Technological, Legal and Ecological

factors affecting manufacturing SME

businesses. The macro environment is

influenced politically for instance, by the

extent to which government operations

affect the economy. Specifically, political

factors include government policies on tax,

trade, tariffs, and general political stability.

These factors may also include goods and

services which the government wants or

does not want provided (Chen, Du, & Chen,

2011)). The political situation of a region or

country in which the manufacturing SMEs

operates will have greater impact on their

existences. The government authorities play

a critical role as the market regulator,

promoter and planner. The government of

the day plays a major role in dictating the

political environment and political stability

as this remains an essential factor that

affects the growth of manufacturing SMEs

(Gathungu & Baariu, 2018). The

philosophy of political parties in power also

influences business practices. Noteworthy

is that the pro-business attitude allows

arrangements such as mergers, acquisition,

joint venture, business allowances and

outsourcing arrangements in between the

manufacturing SMEs.

On the other hand, the legal framework in a

country or region also plays an important

role in how SMEs operate. Legal factors

include law provisions that guide the

general SMEs operations including the

management of labour (Chen et al., 2011).

These factors can affect how a firm

operates, its costs and the demand for its

products. The socio-cultural dimension of

the macro environment includes customs,

way of life and morals that characterize the

society in which the SMEs operate. Social

factors are the cultural aspects that include

health consciousness, population growth

rate, age distribution, career attitudes and

emphasis on safety.

SMEs may change their management

strategies to adjust to the social trends

(Ndife, 2014). Culture is the result of

complex factors that include religion,

language, education and ethical beliefs

where social class is identified by income,

occupation, life style and class norms. A

socio-cultural component of the macro

environment influences the ability of the

SMEs to obtain resources, market its goods

and services, and function within the

society. This enables SMEs to identify the

opportunities and threats for their

businesses (Gathungu et al., 2014). Other

macroeconomic aspects such as economic,

environmental and technological impact the

operations and performance of firms, and

manufacturing SMS in varied ways. The

dynamic nature of the macro environment,

today, provides a challenge for determining

which environment that manufacturing

SMEs need to choose for them to thrive, the

timing of their operations and activities as

well as how to deal with the environmental

challenges. This ever-changing

environment forces organizations to remain

agile in order to survive (McMahon & Carr,

1999).

Richard, Devinney, Yip and Johnson (2009)

define firm performance as the firm’s

ability to achieve planned results as

estimated against its proposed yields and

expected outcomes in terms of financial

performance, market performance and

shareholder return. Moullin (2003) provides

that there is a need to come up with

measures that can be quantified through

identification of aspects of the business




operations or objectives that need regular

evaluation or improvement and whose final

achievements enable the evaluation of the

overall business performance at the end of

the period. These assessments include both

financial and non-financial measures

depending on value, importance and

individual business circumstances.

Common measures of firm performance in

SMEs include financial and non –financial

measures. Financial measures of

performance include return on asset,

investment and equity. Non-financial

measures of performance include

operational efficiency and market share,

employee turnover, entrepreneur

satisfaction and longevity of the firm

(Gentry & Vaidyanathan, 2010). There are

few studies seeking to establish the

moderating effect of macro environment on

the relationship between competitive

strategy drivers and performance especially

in the context of manufacturing SMEs in

Nairobi County. There is therefore a need to

undertake studies in the foregoing area with

a view to enhancing the performance of

these firms.

Literature Review

Entrepreneurship is the process of

identifying an opportunity in the business

environment, pooling of resources,

exploiting the opportunity, make profit and

meeting the needs of customers. It mainly

involves taking risks, creativeness and

being innovative. Several theories explain

entrepreneurship, among them, the

resource-based theory of entrepreneurship

and open systems theory of

entrepreneurship. The resource-based

theory, which largely drives this study

contends that a distinct bundle of resources

that are at the firm’s disposal generate

sustained competitive advantage (Barney,

1991; Conner & Prahalad, 1996). In this

study, the theory conceptualizes the

position that firm performance is enhanced

when firms use and manage their unique

resources to enable the firm attain a

competitive advantage position. The open

systems theory argues that organizational

performance is significantly related to the

vibrant evolutionary nature of fit amid the

business environment and the firm

(Machuki & Aosa, 2011). The theory only

concentrates on the environmental effects

but does not explain the critical role of

competitive strategy drivers, well aligned

with the environmental forces, in enabling a

firm achieve performance.

It can therefore be conceptualized that this

theory can explain the relationship that

arises from the interaction between

competitive strategy drivers that arises from

the macro influence in terms of norms,

culture and policies on environment and

also the entrepreneurial nature of the

owners. The open systems view assumes

that firms are forced to act under situations

of constrained freedom and that they tend to

serve external actors who afford them

resources. While organizations rely on their

environment for critical resources, the

environment is considered to be

unpredictable as it is beyond the firm’s

administrative control. Therefore, firms

tend to build relationships with surrounding

actors in order to reduce uncertainty

(Dubois, 1998). As organizations network

with actors, they often become obligated to

act under conditions of interdependence.

Interdependence is a situation where the

outcome of an action relies on two or more

actors (Pfeffer &Salancik, 1978). As firms

controls varying bundles of resources and

undertake various activities, networks of

firms come up, where no one is in total

control over their own operations.




Perceiving, understanding and responding

to environmental conditions have

implications on competitive strategy drivers

and performance of every firm. Empirical

evidence on the effect of macro business

environment on organizations in terms of

competitive strategy drivers and firm

performance indicate that environment is a

source of opportunities as well as threats for

all organizations (Pearce & Robinson,

2011). Environmental scanning, a critical

aspect in strategy formulation is conducted

to identify important factors and forces that

exist outside the organization and can

possibly specifically or in an indirect way

influence the focused procedures and

execution. To attain a competitive

advantage, firms are required to critically

think and decide on the competitive

advantage that they will try to gain and the

degree to which it will achieve it.

Researches that have conclusively linked

macro environment and firm performance

are few, yet performance depends on

organizations alignment with

environmental changes (Machuki & Aosa,

2011). Literature on the macro environment

of organizations and its direct and indirect

effect on business processes and outcomes

are documented (Osborn & Hunt, 1974).

However, several subsequent studies have

treated macro environment as an

independent variable and performance as

dependent (Machuki & Aosa, 2011;

Venkatraman & Prescott, 1990).

To remain competitive, manufacturing

SMEs must be recognize any stimulus from

the macro environment and ought to

continuously adjust to it. An organization’s

ability to adapt to the changes in the macro

environment will determine its success and

sustainability as well as its survival. There

are many competitive pressures and risks

which hinder the businesses from achieving

their intended goals (Akdogan & Cingoz,

2012). Any manufacturing SME is an open

system between itself and its macro

environment up to a series of relationships

that influence each other (Muhammad,

2014). SMEs influence the macro

environment primarily through their service

or products offerings and are geared toward

relationships with other organizations

making their mark on the community to

which they belong. Analysis of competition

and positioning is essential to realizing

sustainable competitive advantages for

organizations (Selvam, Vanitha, Gayathri,

Bennet, & Nageswari, 2010). The macro

environment includes organizations in the

industry that produce similar products,

suppliers, customers, potential new entrants

and also, products.

Method

Research Procedure and Sample

Characteristics

The study was used the cross-sectional

study design, considered appropriate for

entrepreneurship research (Davidsson,

2004). This design enabled pooling of

quantitative data and allowed the researcher

to identify patterns of association amongst

the variables that confirmed the general

interpretation of the associations among the

study variables. The principal research tool

of data collection of this study was a

structured questionnaire. Respondents

included SME’s owners or senior

managers/persons in charge of the SME

who by virtue of their positions were better

placed to give informed and reliable

responses. The study population comprised

of all manufacturing SMEs in Nairobi

County. The enterprises were in two

categories; category A with between 5-19

employees and category B with between 20-

49 employees. The sample size for the study

was calculated using the formula for finite

population as proposed by Israel (2009).




n = N .

1+N(e2 )

Where:

n= Desired Sample Size

N= Population

e = Margin of Error at 5% (standard value

of 0.05)

The size of the sample in this research will

be:

n = 2050

1+ 2050(0.05)2

n= 334 Manufacturing SMEs

A stratified random was used to establish

proportianate sample from each strata as

follows:

Table 1: Sample Size Determination

Strata Population Sample Percent

Building, Mining and Construction 61 10 3

Chemical and Allied 325 53 16

Energy, Electrical and Electronic 153 25 7

Agriculture and Fresh Produce 49 8 2

Food and Beverages 344 56 17

Leather and Footwear 25 4 1

Metal and Allied 301 49 15

Automotive 129 21 6

Paper and Board 172 28 8

Pharmaceutical and Medical Equipment 123 20 6

Plastics and Rubber 270 44 13

Textiles and Apparel 43 7 2

Timber, Word and Furniture 55 9 3

Total 2,050 334 100

Source: Nairobi County Licensing Office (2019)

Measures

The questionnaire used nominal and ordinal

scaled items on a five-point Likert scale

ranging from Strongly disagree (1) to

Strongly agree (5) and are shown in Table

2, Table 3 and Table 4 below:

The variables and respective measurement

items for macro environment are articulated

under Appendix 1, those of competitive

strategy drivers are under Appendix 2 and

those of firm performance under Appendix

3

On the whole, the measurement instrument

was highly reliable with an overall

Cronbach alpha of 0.813.

Analytical Procedure

To describe the demographic characteristics

of the association and the respondents,




descriptive statistics, that is, frequency and

percentages were used. Manifestation of the

study variables were analyzed using mean,

standard deviation, coefficient of variation,

skewness and kurtosis. To test the normality

of the data, measures of dispersion (SD)

were utilized while factor analysis test was

carried out to reduce the set of study items

to subgroups which could directly be

explained. Inferential measurements were

used to test the data drawn from the

respondents from manufacturing SMEs in

Nairobi County with respect to the stated

hypothesis. The study performed inferential

tests to understand the relationship between

different variables and validate/invalidate

theories. Pearson product of correlation

coefficient was used to measure the

direction and magnitude of relationship

between the study variables. It varied from

-1 to +1. Coefficient of determination (R2)

was used to measure the goodness of fit of

the model. The hypothesis was tested using

hierarchical regression analysis.

Results

Measurement model

Confirmatory factor extraction was done to

confirm the structures for entrepreneurial

orientation measures of innovativeness,

proactiveness, risk taking, competitive

aggressiveness as well as the overall factor

as shown in Table 2 below.

Table 2: Variables and Factor Statistics

Variable Dimension/Structure/Factor

No of

Items

Scale Mean

Scores

Macro Environment

Overall Macro Environment 32 3.76

Political 3 4.12

Economical 8 3.74

Social 9 3.61

Technological 1 3.57

Environmental/Ecological 5 3.82

Legal 6 3.67

Competitive Strategy

Drivers

Overall Competitive Strategy Drivers 14 3.78

Environmental Based Drivers 5 3.87

Resource Based Drives 4 3.55

Hybrid Based Drivers 5 3.92

Firm Performance

(Non-financial)

Overall Firm Non-Financial

Performance 12 3.74

Entrepreneurial Satisfaction 3 3.88

Growth in Employment 4 3.65

Business Longevity 5 3.69




On the whole, our measurement model

shows acceptable good levels of statistical

fit as indicated by the confirmatory factor

analyses.

Table.3: Descriptive Statistics

Response Rate

Frequency Percent

Returned Usable Questionnaires 300 89.82

Returned Non-Usable Questionnaires 5 1.50

Non-Returned Questionnaires 29 8.68

Total 334 100.00

Reliability

Variable Components of

Variables

Cronbach’s

Alpha

No of

items Decision

Competitive strategy drivers

Environmental Based

Drivers, Resource

Based Drivers and

Hybrid Strategy

Drivers

0.812 14 Reliable

Macro environment

Political, Economic,

Social, Technological,

Ecological, Legal

0.909 32 Reliable

Entrepreneurial orientation

Innovativeness,

Proactiveness, Risk

taking and Competitive

aggressiveness

0.731 18 Reliable

Performance of Manufacturing SMEs

Financial measures;

ROI

0.91 12 Reliable Non-financial

measures; Entrepreneur

satisfaction, Growth in

Employee numbers,

business longevity

Overall 0.841 76 Reliable




Age of the Firm

Frequency percent

1-5 years 54 18.16

5-10 years 167 55.5

10-15 years 47 15.6

Over 15 years 32 10.74

Total 300 100%

Ownership

Sole proprietor 266 88.75

Partnership 28 9.21

Company 6 2.05

Total 300 100%

The study achieved a response rate of 89.82

percent which was considered adequate for

further analysis. The measurement

instrument was highly reliable with an

overall Cronbach alpha of 0.813, thus, the

information obtained was reliable and could

be used in decision making. Further

majority of the firms had been in operation

for a period of 5 to 10 years. In terms of

business classification; sole proprietorship

was the most popular model followed by

partnership and companies respectively.




Figure 1: Financial Performance-Return on Investment

There was an upward trend of ROI for the

period 2013-2017 in most of the sectors.

This trend over a period of time suggests

that firms improved their performance with

time and they were responding better to

their macro environment situations.

Positive ROI values show that the firm’s

total returns exceed total costs. It further

shows that firm’s profitability is steadily

rising with time.

Correlation Analysis

0

5

10

15

20

25

2013 2014 2015 2016 2017

RO

I %

Yearstimber, wood and furnituretextile and apparelsplastic and rubberpharmaceutical and medical equipmentpaper and boardsbuilding,mining and constructionchemical and alliedenergy, elecrtical and electronicsagriculture and fresh producefood and beveargesleather and footwearsmetal and allied




Table 4: Correlation between Macro Environment and Performance of Manufacturing

SMEs

Performance Political Economic Social Technological Ecological Legal

Performance Pearson

Correlation 1

Sig. (2-

tailed)

N 300

Political Pearson

Correlation .709** 1

Sig. (2-

tailed) .000

N 300 300

Economic Pearson

Correlation .533** .225** 1

Sig. (2-

tailed) .000 .000

N 300 300 300

Social Pearson

Correlation .498** .185** .670** 1

Sig. (2-

tailed) .000 .000 .000

N 300 300 300 297

Technological Pearson

Correlation .513** .386** .286** .446** 1

Sig. (2-

tailed) .000 .000 .000 .000

N 300 300 300 300 295

Ecological Pearson

Correlation .588** .312** .390** .394** .498** 1

Sig. (2-

tailed) .000 .000 .000 .000 .000

N 300 300 300 300 300 300

Legal Pearson

Correlation .300** .417** .314** .416** .346** .315** 1




Sig. (2-

tailed) .000 .000 .000 .000 .000 .000

N 300 300 300 300 300 300 300

**. Correlation is significant at the 0.01 level (2-tailed).

Source: Field Data (2019)

The Pearson correlation for political and

economic aspects on performance was

significant (r= .709, p=0.000<.05 and r

=.533, P=0.00<.05 respectively). The

correlation for social and technological

aspects on performance was significant (r =

.498, P=0.00<.05 and r = .513, P=0.00<.05

respectively). The Pearson correlation for

ecological and legal aspects on performance

was also significant (r = .588, P=0.000<.05

and r = .300, P=0.000<.05). There exists a

strong positive correlation between the

independent and dependent variables. Thus,

macro environment influences

performance.

Hypothesis Testing

Objective: Macro environment moderates

the effect of competitive strategy drivers on

the performance of manufacturing Small

and Medium Enterprises in Nairobi

County, Kenya

The objective of this study was to determine

effect of macro environments on the

relationship between competitive strategy

drivers and performance of manufacturing

SMEs, hierarchical regression analysis was

used. The equations used to measure the

hypothesis are:

Y1= β0 + β1X + ε

Y2= β0 + β1X+ β2Z+ε

Y3= β0+ β1X+ β2Z+β3 X.Z + ε

Where,

Y1, Y2, Y3 = performance of

manufacturing SMEs.

β0 = constant (intercept),

β1, = coefficients of competitive strategy

drivers

β2= coefficients of macro environment

X= composite index of competitive strategy

drivers

Z=Macro environment

X*Z== Competitive strategy drivers and

macro environment interaction term

ε = Error term

Composite index was computed for

competitive strategy drivers, macro

environment and firm performance.




Table 5: Regression Results for Moderation Results of the Effect of Macro Environment

on competitive strategy drivers and Performance

Model Summary

Model

R

R

Square

Adjusted R

Square

Std.

Error of

the

Estimate

Change Statistics

R

Square

Change

F

Change

df1 df2 Sig. F

Change

1 Competitive

Strategy

Drivers

.352a .124 .121 .61984 .124 42.113 1 298 .000

2 Competitive

Strategy

Drivers,

Macro

Environment

.447a .200 .197 .46321 .200 74.332 1 298 .000

3 Competitive

Strategy

Drivers,

Macro

environment

interaction

.489a .239 .234 .57874 .239 46.568 2 297 .000

ANOVA

Model Sum of Squares df Mean

Square

F Sig.

1 Competitive

Strategy Drivers

Regression 16.180 1 16.180 42.113 .000b

Residual 114.492 298 .384

Total 130.671 299

2 Competitive

Strategy Drivers,

Macro

Environment

Regression 15.949 1 15.949 74.332 .000b

Residual 63.940 298 .215

Total 79.889 299

3 Competitive

Strategy Drivers,

Macro

environment

interaction

Regression 31.195 2 15.597 46.568 .000b

Residual 99.476 297 .335

Total 130.671 299

Coefficients




Model

Unstandardized

Coefficients

Standardized

Coefficients

T

Sig.

Collinearity Statistics

B Std. Error Beta Tolerance VIF

1

2

(Constant) 1.849 .202 9.143 .000

Competitive

Strategy Drivers .390 .060 .352 6.489 .000 1.000 1.000

(constant) 1.608 .151 10.646 .000

Competitive

Strategy Drivers,

Macro

Environment

.387 .045 .447 8.622 .000 1.000 1.000

Competitive

Strategy Drivers,

Macro

environment

interaction

.485 .072 .379 6.696 .000 .800 1.249

a. Dependent Variable: Firm performance

b. Predictors: (Constant), Competitive strategy drivers, Macro environment

Source: Field Data (2019)

This involved three steps of testing

moderating effect. In step one, non-

financial performance was regressed on

competitive strategy drivers. The result

shows a moderate positive relationship

between competitive strategy drivers and

non-financial performance (R= .352a).

coefficient of determination (R2=0.124)

indicated that competitive strategy drivers

accounts for 12.4% of the variation in non-

financial performance. The critical value for

t test had P-value<0.05), thus test statistic is

significant at that level. The results

confirmed the first step of testing

moderation thus moves to step two. In step

two, non-financial performance was

regressed on competitive strategy drivers

and macro environment. The results

indicated that non-financial performance

had a strong positive relationship with

competitive strategy drivers and macro

environment (R= .447a,). Further coefficient

of determination (R2=.200) depicted that


environment explained 20 % of the

variation in non-financial performance. F

value of 74.332 with P-value<0.05)

confirmed that the model is statistically

significant. The results thus suggested that

the second step of testing moderation is

confirmed and the process proceeds to step

three. In step three, interaction term is

introduced. The results showed that

(R2=0.239) 23.9% of the variation in non-

financial performance is explained by the

changes in competitive strategy drivers,

macro environment and interaction term.

The value of the interaction term

(CSD*ME) had a significant influence (β=

.485, P-value = .000<0.05) thus confirming

a moderation effect of macro environment.

The study therefore supports the hypothesis




that macro environment moderates the

effect of competitive strategy drivers on

non-financial performance of

manufacturing SMEs in Nairobi County.

Table 6: Regression Results for Moderation Results of the Effect of Macro Environment

on competitive strategy drivers and Financial Performance (Return on

Investment)

Model Summary

Model R R Square

Adjusted

R

Square

Std. Error of the

Estimate

1 Competitive strategy

drivers 0.323 0.104 0.081 0.0374771

Competitive strategy

drivers 0.351 0.123 0.076 0.0375788

2 Macro environment

3

Competitive strategy

drivers

0.358 0.128 0.055 0.0379914 Macro environment

Interaction term

ANOVA

Model Sum of

Squares df Mean Square F Sig.

1 Competitive

strategy drivers

Regression 0.006 1 0.006 4.418 0.042

Residual 0.298 298 0.001

Total 0.304 299

2

Competitive

strategy drivers Regression 0.007 2 0.004 2.594 0.088

Macro

environment Residual 0.297 297 0.001

Total 0.304 299

3

Competitive

strategy drivers Regression 0.008 3 0.003 1.759 0.001

Macro

environment Residual 0.296 296 0.001




Interaction term Total 0.304 299

Coefficients

Model

Unstandardized

Coefficients

Standardized

Coefficients t Sig.

B Std. Error Beta

1

(Constant) -0.006 0.032 -0.182 0.857

Competitive

strategy

drivers

0.019 0.009 0.323 2.102 0.042

(Constant) -0.027 0.04 -1.669 0.007

Competitive

strategy

drivers

0.015 0.01 0.26 1.936 0.003

2 Macro

environment 0.009 0.011 0.151 0.891 0.379

3

(Constant) 0.038 0.151 0.254 0.801

Competitive

strategy

drivers

-0.006 0.048 -0.094 -2.117 0.008

Macro

environment -0.009 0.042 -0.144 -2.212 0.003

Interaction

term 0.006 0.013 0.552 2.448 0.007

Model 1 Predictors (Constant) Competitive strategy drivers

Model 2 Predictors: (Constant) Competitive strategy drivers and macro environment

Model 3 Predictors: (Constant) Competitive strategy drivers, macro environment and

Interaction term.

Dependent Variable: Return on Investment

Source: Field Data (2019).

Stepwise method used involved three steps

of analysis. In step one, financial

performance was regressed on competitive

strategy drivers. The result shows that the

association between competitive strategy

drivers and ROI is significant (R= .323a,

R2=0.104, P-value<0.05). The test statistic

is significant at this level, thus proceeds to

step two. In step two, financial

performance was regressed on competitive

strategy divers and macro environment. The

results indicated that the association




between competitive strategy divers, macro

environment and financial performance is

significant (R= .351a, R2=.123, P-

value<0.05). the results, thus, suggested

that the second step of testing moderation is

satisfied and the process proceed to step

three. In step three, the interaction term was

introduced to test on moderation. The

results indicated that competitive strategy

drivers, macro environment and interaction

term accounts for 12.8% of the variation in

financial performance (R= .358a, R2=0.128,

P-value<0.05). Further, the value of the

interaction term (CSD*ME) had a

significant influence (β=.006, P-value =

.007<0.05) thus confirming a moderation

effect of the macro environment. The

hypothesis that macro environment

moderates the relationship between

competitive strategy drivers and financial

performance of manufacturing SMES was

supported.

Table 7: Summary of Regression Results for Moderating Influence of Macro

Environment

Hypothesis Results Hypothesis Remarks

H1 Macro environment

moderates the effect of

competitive strategy

drivers on the non-

financial performance of

manufacturing SMEs in

Nairobi County, Kenya.

R2=0.239

F= 46.568, P-Value=

0.000<0.05

β= 0.485, t= 6.696, P-

Value=0.000<0.05

Supported

H1 Macro environment



drivers on financial

performance (return on

investment) of



R2=0.128

F= 1.759, P-Value=

0.01<0.05

β= 0.006, t= 2.448, P-

Value=0.007<0.05

Supported

H1a Political environment



drivers on the

performance of



R2=0.177

F= 31.919, P-Value=

0.000<0.05

β= 0.005, t= 2.500, P-

Value=0.001<0.05

Supported

H1b Economic environment



drivers on the

performance of

R2=0.131

F= 30.739, P-Value=

0.000<0.05 Supported






β=0.115, t= 3.710, P-

Value=0.001<0.05

H1c Social environment



drivers on the

performance of



R2=0.151

F= 30.323, P-Value=

0.001<0.05

β= 0.221, t= 2.146, P-

Value=0.002<0.05

Supported

H1d Technological

environment moderates

the effect of competitive

strategy drivers on the

performance of



R2=0.209

F= 34.202, P-Value=

0.003<0.05

β=0.322, t= 3.188, P-

Value=0.000<0.05

Supported

H1e Ecological environment



drivers on the

performance of



R2=0.221

F= 27.992, P-Value=

0.000<0.05

β=0.278, t= 2.122, P-

Value=0.000<0.05

Supported

H1f Legal environment



drivers on the

performance of



R2=0.110

F= 33.544, P-Value=

0.000<0.05

β=0.116, t=0.899, P-

Value=0.073>0.05

Not supported

The results above indicate that macro

environment moderates the relationship


firm performance except for the legal

environment. All the macroeconomic

aspects except the legal one individually

moderates the relationship between


performance.

Conclusion

The objective of the study was to assess the

effect of macro environment on the

relationship between competitive strategy

drivers and the performance of

manufacturing SMEs. In order to achieve

this objective, a hypothesis which stated

that macro environment moderates the

effect of competitive strategy drivers on the

firm performance was formulated. Macro

environment was operationalized into six




aspects, that is, political, economic, social,

technological, Legal and Ecological.

Moderation takes effect if the interaction

term between the moderator variable and

independent variable in the model is

significant (p-value<0.05). The study

findings supported the hypothesis that the

macro environment moderates competitive

strategy and firm performance relationship.

Political, economic, social, technological

and ecological aspects were found to

individually moderate the relationship


firm performance. Legal aspect on its own

did not moderate the relationship between


performance. This finding concurs with the

resource-based theory of entrepreneurship

which argues that unique bundle of

resources at the disposal of the firm

generate sustained competitive advantage

(Barney, 1991; Conner & Prahalad, 1996).

Thus, entrepreneurs develop strategies

based on the resources available in the firm

and also based on the environmental

conditions and also the pro-activeness and

innovative nature of the entrepreneurs.

Resources that are valuable, rare, and

difficult to imitate or substitute are deemed

to be strategic enough to offer sustainable

competitive advantage (Barney, 1991).

Such strategic resources strengthen

organizations’ ability to uniquely

differentiate their products or service

offerings. These results showed that owners

of manufacturing SMEs need to scan their

political, economic, social, technological

environment as they adopt various

competitive strategy drivers so as to

enhance performance as proposed by

Conner (1991). Developing strategies in an

industry is the starting point for achieving

competitive advantage. However, firms do

not operate in a vacuum, they instead

operate in a n environment macro

environment creates both opportunities and

threats which then influence the strategies

adopted by the firms. Rohitratana and

Boon-Itt, (2011), argues that the present

business environment conditions cannot

allow companies to ignore the key impacts

of value for its focused position. Thus,

owners of manufacturing SMEs should take

cognizant of customers, competitors,

suppliers facilitate alignment between


environments.

Recommendations

The results revealed significant relationship

between macro environment as a moderator

of the relationship between competitive

strategy drivers and performance of

manufacturing SMEs. This indicates that

owners of manufacturing SMEs should

embrace scanning of the environment in

which they operate as they formulate their

strategies. It was also revealed that legal

aspect of macro environment does not

moderate the relationship between

competitive strategy drives and

performance. On this basis, the study

recommends further analysis so as to find

the reasons behind this insignificant

moderating effect. Further manufacturing

SMEs across the board should adapt

appropriate competitive strategy drives in

line with the environment in which they

operate which would increase their

performance. The present study relies on a

single informant who had knowledge of the

firms’ activities and their level of

commitment. The use of multiple

respondents from each firm is preferable

and would provide additional data.

Limitations of the Study

This study had limitations encountered in

the process of writing the report. The

research was limited to a descriptive cross-




sectional survey approach method for

which data was collected analyzed at

interpretation done at a specific time a cross

all the sampled SMEs as opposed to

longitudinal approach which allows for

collection, analysis and interpretation of

data over a period of time. Secondly only

one respondent for the target SMEs in the

manufacturing sector was used. This

required that the respondent be much

conversant with the operations of the SMEs

in the manufacturing sector. The research

was also aimed at SME owners/managers

who would not always have time to

respond. Further, the research only focused

on manufacturing SMEs in Nairobi County

thus there is a need to extend the research to

other counties for comparison of findings.

The questionnaire tool used was also a

limitation as it had closed ended questions

only. We encourage that future research

work deploys both open and closed ended

questions to allow for deeper insights into

the issue being studied. Future research may

also consider qualitative research

approaches to examine the above issues as

they would provide useful insights and

complement already existing quantitative

approaches in this area.

Acknowledgement

The author would like to thank the

University of Nairobi, main campus for

allowing access to the library facilities

including their very supportive staff

members.

Author Notes

This journal is an extract from the author’s

PhD thesis undertaken at the School of

Business, University of Nairobi, Kenya.

Declaration

The authors declare that they have no

conflict of interest with respect to research,

authorship or publication.

Funding

This research received no specific grant

from any funding agency in the public,

commercial, or not-for-profit sectors.

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APPENDICES

Appendix 1 : Variables and respective measurement items for Macro Environment

Political Aspect

1 The political stability of the country

2 Change of political regime

3 The country’s overall political stability

Economic Aspect

1 Inflationary trends in the country

2 Level of the country’s overall economic development

3 Foreign exchange rates

4 Interest rates

5 Availability of credit

6 Changes in the taxation regime

7 Annual budget allocations to the firm

8 Intermittent budget reviews and re-allocations by government

Social Aspect

1 Societal norms and values

2 Customs of various communities

3 Religion of host communities

4 Demands of host communities

5 Cultural practices e.g. land demarcation, farming practices,

pastoralism, etc.

6 Population growth rate

7 Crime rates and terrorism

8 Tribal inclinations

9 Gender issues

Technological Aspect

1 Developments in Information Communication & Technology e.g.

internet, digitization of services etc.

Legal Aspect

1 Interest from various stakeholders




2 Government pronouncements on changes in policy from time to time

3 Devolved Government structure

4 Occurrences in the natural environment e.g. floods, drought etc.

5 Civil society firm’s agitation for rights

Legal Aspect

1 Government’s fiscal policies

2 Taxation policies

3 Changes in the Kenya Constitution 2010 and subsequent legislation

4 The legal framework prescribing the mandate of the firm

5 Legislative activities touching on the firm’s business

6 Environmental legislation




Appendix 2 : Variables and respective measurement items for Competitive strategy

drivers

Environmental based drivers

1 We have the ability to deliver high quality products and

services

2 We have effective sales and marketing team

3 The market understands the benefits offered by the

differentiated offerings

4 Products and services different from and more attractive than

those of our competitors

5 We have brand image that our customers value

6 We concentrate on particular niche markets

7 We understand the dynamics of the niche market and the

unique needs of customers within it

8 We build strong brand loyalty amongst our customers thus

making our particular market segment less attractive to

competitors

9 We offer unique features that fulfill the demands of a narrow

market

10 The firm concentrate on a particular market

11 The firm charge low prices relative to other firms that compete

within the target market

12 The firm practice the lowest cost of operation in the industry

13 Our production process is backed by innovation

14 The firm acquire quality raw materials at the lowest price

15 The firm produces highly standardized product using advanced

technology

Resource based drivers

1 Our firm can easily mobilise resources

2 Our firm has a strong business plan

3 Our firm has clear strategy and competitive edge

4 Our management team are competent and valuable

5 Our business valuation and scalability are in line with investors

needs

6 Our firm embraces the development of individual and

institutional ingenuity

7 Digitization of performance management not only provides

more precise data but also positively influences management

process

8 Technology facilitate a culture of continuous feedback thus

everyone knows where they stand on a regular basis

9 Technology enables collection of more objective performance

data on a real time basis




10 Our firm has high skilled labour so as to produce economic

value

11 Human capital is the most essential capital in our firm

12 The firm value knowledge, experience, skill, attitudes,

abilities, behaviour and obligation of employees

13 The ability to effectively acquire, control and utilize

knowledge in every business activity is the differentiator

between our firm and competitors

14 A tool of managing increasingly complex global value chain

networks

15 The firm focuses on optimizing volumes and value based on

cross functional management

16 The firm integrate decision making throughout the value chain

Hybrid strategy drivers

1 Our firm achieve both high quality and productivity at the same

time

2 Our firm embraces mass customizations

3 Our firm makes consistent low cost strategic decisions on how

to pursue competitive advantages and align resources and

capabilities

4 It is a way of responding to changes in the competitive

environment more flexibly and effectively and stay

competitive




Appendix 3 : Firm Performance

Entrepreneur Satisfaction

1 You are generally satisfied with your current business

2 Your current business meets your expectations

3 Your current business is your most ideal

Growth in Employment

1 Number of employees have significantly increased in line with our

business expansion

2 Local market plays a role in employment growth

3 Our firm promotes and hire new employees annually

4 Our firm experience low employee turnover annually

Business longevity

1 Financial strength influences our longevity

2 Customer orientation determine business lifespan

3 Internal capabilities influence our longevity

4 Strategic perspective defines our firm lifespan

5 Learning and growth influences our firm longevity

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